The objective of research is to discover the "'true' relationship between the predictor and an outcome" (The University of Texas Health Science Center at San Antonio Family & Community Medicine, "Bias in Research"). In using the term predictor, we are referring to the variable that is changed over time to reach results; the predictor is also called the independent variable. By outcome, we mean the results reached by having manipulated the predictor; outcome (variable)...

The objective of research is to discover the "'true' relationship between the predictor and an outcome" (The University of Texas Health Science Center at San Antonio Family & Community Medicine, "Bias in Research"). In using the term predictor, we are referring to the variable that is changed over time to reach results; the predictor is also called the independent variable. By outcome, we mean the results reached by having manipulated the predictor; outcome (variable) is also called the dependent variable. Bias in research refers to anything that changes the research results in unexpected ways that prevent "unprejudiced consideration of a question."

There are many different types of bias that can affect many different aspects of the validity of a study. Bias can affect the internal validity, meaning the truth within the study itself; the external validity, meaning the way in which a study can be generalized to apply towards a larger population; the construct validity, meaning the ways in which variables are accurately measured; and the statistical validity, meaning the ways in which the statistics show accurate results.

Authors A. Rahman et al. (2007) conducted a complex study titled "Association of Arsenic Exposure during Pregnancy with Fetal Loss and Infant Death: A Cohort Study in Bangladesh" in which they assessed the affect of arsenic exposure through drinking water in tube wells on fetal and infant mortality and determined that prenatal arsenic exposure increases the risk of infant death but not of fetal death. The study was complex because it included so many covariates, meaning a variable that can affect the outcome. Covariates included "order of pregnancy, mother's education, [and] socioeconomic status," as well as calendar year and location.

The researchers did an excellent job of accounting for anything that could affect the measurements. Nevertheless, they do admit to some possible biases with respect to the accuracy of the data concerning the levels of arsenic concentrations present in the drinking water. Such biases will affect the internal validity of the study.

The scientists attempted to reduce biases by cross-checking measurement data. First, they used data on arsenic measurements collected by the demographic surveillance system (HDSS) in Matlab, Bangladesh, during an independent study on the health effects of arsenic exposure conducted in a 2002 - 2003 survey. Researchers of the 2002 - 2003 study conducted the survey by going door to door and interviewing family members about their drinking water history. A second team of researchers in the 2002 - 2003 study collected water samples from all active tube wells. Then, Rahman et al. (2007) cross-checked the data from the survey using data previously collected in HDSS socioeconomic censuses conducted in 1874, 1982, and 1996.

While all three of these methods do help eliminate biases, the researchers were relying on interviewees' statements and data conducted some years before their own study, which can affect the internal validity of a study.

The reliance of door-to-door statements made by interviewees can affect internal validity by producing maturation bias. Maturation bias occurs when test subjects mature either physically or cognitively as the study continues. Answers to survey questions may change over time as the test subjects develop more of an understanding of the results the researchers expect. In addition, the gaps in years between when data on arsenic exposure was collected and when the researchers used the data to draw conclusions can affect the internal validity of the study by potentially creating history bias. History bias occurs do to the fact that the passing of time can change results. History bias, though not applicable in this case, especially refers to the fact that the occurrence of dramatic historical events can change results.

One of the most striking areas of bias, that I find, is the country in which this study was performed. Given the time frame of the study, Bangladesh could easily have been considered a prime area for high infant mortality. It was one of the most populous, poverty-stricken areas in the world, at that time.

Another possible source of bias would be the age of the women. Could these losses and deaths be contributed, in part, to the women being extremely young or elderly?

There is the question of general health to consider, also. As I stated, this was a perilous time for this country. Poverty and overpopulation breed disease. I see no mention of the comparative health of these mothers.

There is also the lack of any documentation of abuse of these pregnant women. Were they victims of spousal abuse? Could they be the victims of overwork and exhaustion? Could their mental conditions have contributed to them not being able to support full-term, healthy pregnancies?